CN106709216A - Method for optimally designing microphone array by taking acoustic propagation correlation loss into consideration - Google Patents
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Abstract
The invention provides a method for optimally designing a microphone array by taking acoustic propagation correlation loss into consideration. According to the method, a correlation loss model is established for the problem of correlation loss occurring when an acoustic wave is propagated at a long distance in a civil aircraft flight test, and evaluation for the array side-lobe suppression level and resolution performance parameter is carried out based on the model; and optimizing coordinates of an array unit by taking the array side-lobe suppression level and resolution performance parameter under influence of correlation loss problem as objective functions and utilizing a quick non-dominated sorting genetic algorithm. The method overcomes the defect in an existing method, can be used for providing the array unit arrangement with comprehensively optimized side-lobe suppression level and resolution and still has excellent performance under the influence of correlation loss problem, and is adaptive to the optimal design of an array for developing pneumatic noise measurement of a microphone phase array for flight tests.
Description
Technical field
Seating plane flight test is applied to the present invention relates to a kind of microphone array optimum design method, particularly one kind
The microphone array optimum design method of aerodynamic noise measurement.
Background technology
Carry out aerodynamic noise measurement in seating plane flight test using Mike's wind facies array measurement technology, can obtain
Most abundant, most true, most reference value civil aircraft aerodynamic noise data, are the important rings of civil aircraft Study of Aerodynamic
Section, it is of increased attention at home.In civil aircraft flight test, atmospheric environment is complicated, and sound wave is from aircraft
Send, through the propagation of long-distance, the correlation between the acoustic signals that the microphone of diverse location is received in array weakens.
Here it is acoustic propagation correlation loses problem, can be with propagation distance increase, frequency of sound wave rising and meteorological condition variation
Become serious.Array has two main performance index:Resolution ratio and Sidelobe Suppression level.Resolution ratio is relevant with array bore, mouth
Footpath is bigger, and resolution ratio is more excellent.Sidelobe Suppression level is relevant with the density degree that array element is arranged, array is arranged to closeer, side
Valve suppression level is more excellent.Under the influence of acoustic propagation correlation loss problem, the wheat in array in the range of a less bore
It is related between acoustic signals received by gram wind, and between signal received by microphone outside this scope
Correlation weakens, even totally uncorrelated.Osculum is partitioned into equivalent to by the array more than a heavy caliber, microphone number
The few array of footpath, microphone number, causes array effective aperture, effective microphone number significantly to reduce, and resolution ratio and secondary lobe press down
Level processed is drastically reduced.
Therefore, aerodynamic noise measurement, battle array are carried out using Mike's wind facies array measurement technology in civil aircraft flight test
Have to consider that acoustic propagation correlation loses problem in row design.
Array Design is exactly the optimization array unit arrangement under the conditions of physical constraint, array performance index is being treated frequency measurement
It is satisfied by requiring in the range of rate.Existing array optimization method for designing fails to be lost in view of acoustic propagation correlation in the design process
Mistake problem, given microphone array is designed when civil aircraft aerodynamic noise flight test is applied to, and array performance is drastically reduced,
Requirement of the civil aircraft aerodynamic noise flight test to array performance can not be met.
The content of the invention
Present invention solves the technical problem that being:Overcome the shortcomings of existing method, there is provided one kind considers acoustic propagation correlation
The microphone array optimum design method of loss, the method is propagated for sound wave in civil aircraft flight test and occurred through long-distance
Correlation lose problem, set up correlation and lose model, and array Sidelobe Suppression level and resolution ratio are carried out based on this model
The assessment of two performance parameters, is target letter with array Sidelobe Suppression level and resolution ratio under the influence of correlation loss problem
Number, using the multi-objective optimization algorithm of non-dominant genetic algorithm II, optimization array unit coordinate.
The technical scheme is that:It is a kind of to consider the microphone array optimum design method that acoustic propagation correlation is lost,
Comprise the following steps:
(1) genetic algorithm parameter is set;Randomly generate father population PtInterior array co-ordinates, wherein footmark t represent hereditary calculation
Algebraically in method;If the initial value of t is 0, if sub- population QtIt is empty set;The genetic algorithm parameter includes:Number of individuals in population
Mesh N, select probability, crossover probability, mutation probability, maximum iteration;
(2) father population P is mergedtWith sub- population QtObtain population Ut={ Pt∪Qt, calculate population UtInterior all arrays are in sound
Propagate the resolution ratio and Sidelobe Suppression level under the influence of correlation loss problem;
(3) population U is calculatedtThe sequence value of interior array non-dominated ranking and array crowding distance;
(4) population U is judgedtWhether interior array meets constraints, and the sequence value that will be unsatisfactory for the array of constraints is set
It is 2N;
(5) the sequence value and crowding distance of the non-dominated ranking of foundation array cut out and father population PtPopulation invariable number identical
New population Pt+1;
(6) new population selected, intersected and mutation operation, obtained sub- population Qt+1;
(7) whether t+1 is judged more than maximum iteration, if then output father population Pt+1Middle sequence value is 1 all non-branch
Ligand array terminates as optimal solution set, and this method;Otherwise, t=t+1, goes to step (2).
In the step (2) when Wave beam forming figure is calculated, weight coefficient is introduced to each microphoneWherein:Erf (x) is Gauss error function;riFor in i-th microphone distance arrays
The distance of the heart;RfIt is effective array bore, its computing formula is:Wherein h is the distance between sound source and array,
I.e. aircraft flight is highly;F is analysis frequency.
The obtaining value method of array resolution and Sidelobe Suppression level is in the step (2):Calculate in analysis frequency range
Array resolution and Sidelobe Suppression level under all 1/3rd octave center frequencies, take wherein worst array resolution
With Sidelobe Suppression level.
Constraints in the step (4) is:xi∈ Ω and | xi-xj|≥d;Wherein xi、xjIt is array element coordinate, M
It is array element number, i=1....M, j=1....M, d are minimum spacing limit value between unit, Ω is determined by array bore
Region.
Compared with the prior art, the invention has the advantages that:
(1) present invention establishes an acoustic propagation correlation loss model, and embedded array process of optimization can be given at
Remain to keep the array element of preferable array performance to arrange under the influence of acoustic propagation correlation loss problem, can preferably meet in the people
Carry out demand of the aerodynamic noise measurement to array using Mike's wind facies array measurement technology with aircraft flight test.
(2) present invention employs multi-objective optimization design of power method, array resolution and Sidelobe Suppression level can be provided most
Excellent disaggregation, can choose most suitable array element arrangement in optimal solution set according to the actual requirements.
(3) present invention by using microphone minimum spacing as optimization problem constraints so that given
The microphone that array element arrangement is adapted in actual array arrangement is installed.
(4) when using institute's array out is designed by this method, introducing consideration acoustic propagation phase in data handling procedure
The microphone weight coefficient that closing property is lost, it is possible to increase array performance.
Brief description of the drawings
Fig. 1 is array optimization method for designing flow chart of the present invention.
Fig. 2 is the schematic diagram of array plane and sound source surface grids.
Fig. 3 is the Wave beam forming schematic diagram of the unit sound source of array.
Specific embodiment
The present invention sets up acoustic propagation correlation and loses model for the measurement of civil aircraft flight test aerodynamic noise.In sound
Propagate under the influence of correlation loss problem, the sound wave received by the microphone in array in the range of a less bore is believed
Number it is related, and the microphone signal outside this scope is incoherent, therefore introduce array effective aperture:
Wherein:RfIt is effective array bore;H be distance between sound source and array, i.e. aircraft flight highly;F is analysis frequency
Rate.When i.e. flying height is 50m, the array effective aperture under 4000Hz frequencies is 1m.Weight coefficient is introduced to each microphone, it is embedding
Enter array performance evaluation process:
Wherein:wi,fIt is weight coefficient of i-th microphone under analysis frequency f;Erf (x) is Gauss error function;ri
It is the distance at microphone i distance arrays center.
Array multi-objective optimization question is modeled:
min{f1(xi),f2(xi)}
f1(xi)=R (xi), (xi∈ Ω, | xi-xj|≥d);
f2(xi)=MSL (xi)
Wherein:R(xi) and MSL (xi) it is object function, wherein R (xi) it is to consider the resolution that acoustic propagation correlation is lost
Rate performance function, MSL (xi) it is to consider the array Sidelobe Suppression level function that acoustic propagation correlation is lost;xi(i=1....M) it is
Array element coordinate, M is array element number;The region that Ω is determined by array bore;|xi-xj| >=d is constraints, single
Minimum spacing is not less than setting d between unit.
As shown in figure 1, a kind of stream for considering the microphone array optimum design method that acoustic propagation correlation is lost of the present invention
Cheng Tu, comprises the following steps that:
(1) set genetic algorithm parameter (individual amount N in population, select probability ss=0.9, crossover probability sc=0.9,
Mutation probability sm=0.01, maximum iteration G).Randomly generate initial population Pt={ xi n(t), (i=1 ..., M), (n=
1 ..., N) in array co-ordinates, M is microphone number, and footmark t represents the algebraically in genetic algorithm;Wherein xi nT () is n-th
Whole array co-ordinates of individual array, represent array n.If algebraically t=0, QtIt is empty set.
(2) father population P is mergedtWith sub- population QtObtain population Ut={ Pt∪Qt, calculate population RtInterior all arrays are in sound
Propagate the resolution ratio and Sidelobe Suppression level under the influence of correlation loss problem.
Because population merges so that all arrays of parent population are all in UtIn, it ensure that the elite array of parent is protected
Stay, i.e. elite retention strategy.
Sidelobe Suppression level, also referred to as maximum side lobe levels, be array unit sound source Wave beam forming figure in secondary lobe most
Big to be worth, computing formula is:
Wherein:
F is analysis frequency, fLIt is analysis lower-frequency limit, fHIt is analysis upper frequency limit;
D is sound source surface grids region, and usually parallel to the plane domain of array plane, region D ' is that region D rejects master
The region of valve, sound source surface grids are as shown in Figure 2 with the schematic diagram of array plane.yjIt is sound source surface grids coordinate;J is natural number;
psf(xi n,yj, f) it is array xi nAnalysis frequency f under unit sound source Wave beam forming figure.
psf(xi n,yj, f) it is array xi nTo sound source face position yjFrequency for f unit sound source analysis frequency be f
Under Wave beam forming figure, describe the basic response of array.Fig. 3 is the Wave beam forming schematic diagram of the unit sound source of array, in figure
Horizontal coordinate face is sound source face, and the unit of two axles is m, and the longitudinal axis is psf, and unit is dB.1 is main lobe in figure, and 2 is secondary lobe.Its
Calculation procedure is that unit point sound source is placed in sound source surface grids, is typically placed in a certain position y directly over array center0, meter
The cross-spectrum matrix that the point sound source causes on array is calculated, Wave beam forming is then carried out, computing formula is:
Wherein:
A is in position y0The cross-spectrum matrix that the unit sound source at place causes on array, computing formula is:
T is adjustment vector, and computing formula is:
wi,fFor the microphone weight coefficient that above-mentioned consideration acoustic propagation correlation is lost;
" * " represents that plural number takes conjugation.
Psf is in y0Position obtains maximum zero, thus maximum side lobe levels are minus value.
Array resolution takes from the main lobe width of main lobe peak-fall 3dB positions, and its computing formula is:
R(xi)=2min (| yj-y0|),psf(xi,yj, f)=- 3dB;
The Sidelobe Suppression level and resolution ratio of array are relevant with analysis frequency, it is contemplated that acoustic propagation correlation loses degree
Also it is relevant with frequency, array Sidelobe Suppression level and resolution ratio of the array in whole analysis frequency range need to be examined or check.Generally only
Sidelobe Suppression level and resolution ratio under the centre frequency of whole 1/3rd octaves in analysis frequency range are calculated, is taken most bad
Value is used as object function.
(3) population U is calculatedtThe sequence value and crowding distance of interior array non-dominated ranking
By non-dominated ranking method, compare population UtIn each array target function value, find out the non-dominant of current population
Disaggregation cooperation is the PS that sequence value is 11, by PS1In all arrays removed from current population, found out again in remaining array group new
Non-dominant disaggregation as the PS that sequence value is 22, the rest may be inferred, is calculated and order of classification until all arrays all complete sequence value.
Non-domination solution set is exactly the set being deconstructed into not being dominant by other solutions, and all solutions in solution set are
Non-dominant relation.
Non-dominant contextual definition is, for meeting following two situations simultaneously, to solve xi AConciliate xi BCannot compare and be non-branch
With relation.OobjIt is object function total number, context of methods Oobj=2.
1) at least for some object function, xi BCompare xi AIt is good, i.e.,:There is l, 1≤l≤OobjSo that fl(xi B)<fl
(xi A)。
2) at least for some object function, xi ACompare xi BIt is good, i.e.,:There is l, 1≤l≤OobjSo that fl(xi A)<fl
(xi B)。
Crowding distance (Crowding Distance) dk,nIt is defined as, is the non-domination solution set PS of k in sequence valuekIn, with
Two array n adjacent arrays n+1, n-1, the dimensionless of the difference of each target function value it is average and.It is so-called it is adjacent refer to
Two minimum arrays of certain sub-goal l Euclidean distances of array n.Non-domination solution set PSkIn array n crowding distance, its
Expression formula is:
In formulaWithIt is non-domination solution set PSkIn array n+1, n-1 in l-th functional value of target,WithIt is non-domination solution set PSkIn l-th object function of all arrays maximum, minimum value.
Crowding distance sort algorithm is, in same non-dominated ranking grade, array presses the descending sequence of crowding distance.
The crowding distance of array is bigger, illustrates that the region residing for it is more sparse, then the array in the region is more valuable, in selection course
In preferentially retain.
(4) population U is judgedtWhether interior array meets constraints xi∈ Ω and | xi-xj| >=d, constraints will not met
The sequence value of array be set to 2N.
(5) the sequence value and crowding distance of the non-dominated ranking of foundation array, cut out and father population PtPopulation invariable number is identical
New population Pt+1.From UtTop n array is selected as population P of future generationt+1.If forward position set PS1Quantity be less than N, then
PS1Array can all enter population P of future generationt+1.Population Pt+1In other arrays will sequentially successively from non-domination solution
Collection set PS2, PS3... ... PSkMiddle selection, if until further selecting PSkAfterwards population quantity will more than N when, then be changed to row
Sequence grade is the non-domination solution set PS of kkRequired array is selected by crowding distance sort algorithm, and is included into new parent kind
Group Pt+1。
(6) evolutional operation.Selection operation is carried out using algorithm of tournament selection strategy, and sub- population to choosing becomes
Different and crossover operation.
Championship method choice strategy every time from population take out certain amount array, then select wherein best one
Into progeny population.The operation is repeated, until new population scale reaches original population scale.Specific operating procedure is such as
Under:
1) the array quantity N of selection every time is determined according to select probabilityt, formula is Nt=ss × N.
2) from population Pt+1Middle random selection NtIndividual array (the selected probability of each array is identical) composition group, according to each battle array
The sequence value and crowding distance of the non-dominated ranking of row, therefrom select sequence value minimum or when sequence value phase simultaneous selection crowding distance is maximum
Array enter population Q of future generationt+1。
3) repeat step 2) until population Q of future generationt+1Number reaches N
For the population Q of future generation for completing to be obtained after selectiont+1, intersected and mutation operation, handed over using simulation binary system
Fork (Simulated Binary crossover, SBX) method.Q is selected according to crossover probabilityt+1In two array xi AAnd xi BEnter
Row crossing operation, produces two new array xi′AAnd xi′B.System of selection is to judge whether to meet u≤sc, and u is interval (0,1)
Interior equally distributed random number.SBX crossing operations employ equation below generation
β is breadth coefficient in formula, is tried to achieve by following formula:
In formula, u is (0,1) interval interior equally distributed random number;ηcIt is transposition index, is a constant, chooses 10.Pass through
Two arrays to selecting carry out SBX intersections under each dimension, just can obtain two new individualities.
Q is selected according to mutation probabilityt+1Middle array xi nMutation operator is carried out, new array x is producedi′n.System of selection is to judge
Whether u≤sm is met, and u is (0,1) interval interior equally distributed random number.SBX mutation operators employ equation below generation
xi′n=xi n+δq(xi UB-xi LB);
δ in formulaqIt is variation side-play amount, is tried to achieve by following formula:
In formula, u is (0,1) interval interior equally distributed random number;ηmIt is index of variability, is a constant, chooses 20.xi UB
And xi LBIt is the bound of variable.δ is to become the minimum value in span bound dimensionless distance.
(7) algorithm terminates judging.Whether t+1 is judged more than maximum iteration G, if then output father population Pt+1Middle sequence
The all non-dominant arrays being worth for 1 terminate as final Pareto optimal solution sets, and algorithm;Otherwise, t=t+1, goes to step
(2)。
Operation principle of the invention:Under the influence of acoustic propagation correlation loss problem, in a less bore in array
In the range of microphone received by acoustic signals be related, and the microphone signal outside this scope is uncorrelated
, therefore, introducing and lost the array effective aperture that degree is influenceed by acoustic propagation correlation, acoustic propagation correlation is lost degree and is got over
Greatly, then array effective aperture is smaller.And microphone weight coefficient is constructed with this, the weight system of the microphone in array effective aperture
Number is big, and the weight coefficient of the microphone in array effective aperture is small, simulates acoustic propagation correlation Loss.And passed based on this sound
Broadcasting correlation loss model carries out the assessment of array Sidelobe Suppression level and resolution ratio performance parameter, and problem shadow is lost with correlation
Array Sidelobe Suppression level and resolution ratio under ringing are object function, using quick non-dominated sorted genetic algorithm, optimization array
Unit coordinate, so that obtaining array Sidelobe Suppression level and resolution ratio under the influence of correlation loss problem meets requiring, suitable
For the array of flight test aerodynamic noise measurement.
Claims (4)
1. it is a kind of to consider the microphone array optimum design method that acoustic propagation correlation is lost, it is characterised in that including following step
Suddenly:
(1) genetic algorithm parameter is set;Randomly generate father population PtInterior array co-ordinates, wherein footmark t are represented in genetic algorithm
Algebraically;If the initial value of t is 0, if sub- population QtIt is empty set;The genetic algorithm parameter includes:Individual amount N in population;
(2) father population P is mergedtWith sub- population QtObtain population Ut={ Pt∪Qt, calculate population UtInterior all arrays are in acoustic propagation
Resolution ratio and Sidelobe Suppression level under the influence of correlation loss problem;
(3) population U is calculatedtThe sequence value of interior array non-dominated ranking and array crowding distance;
(4) population U is judgedtWhether interior array meets constraints, and the sequence value that will be unsatisfactory for the array of constraints is set to 2N;
(5) the sequence value and crowding distance of the non-dominated ranking of foundation array cut out and father population PtPopulation invariable number identical novel species
Group Pt+1;
(6) new population selected, intersected and mutation operation, obtained sub- population Qt+1;
(7) whether t+1 is judged more than maximum iteration, if then output father population Pt+1Middle sequence value is 1 all non-dominant battle array
Row terminate as optimal solution set, and this method;Otherwise, t=t+1, goes to step (2).
2. the microphone array optimum design method that a kind of consideration acoustic propagation correlation according to claim 1 is lost, its
It is characterised by:In the step (2) when Wave beam forming figure is calculated, weight coefficient is introduced to each microphoneWherein:Erf (x) is Gauss error function;riFor in i-th microphone distance arrays
The distance of the heart;RfIt is effective array bore, its computing formula is:Wherein h is the distance between sound source and array,
I.e. aircraft flight is highly;F is analysis frequency.
3. the microphone array optimum design method that a kind of consideration acoustic propagation correlation according to claim 1 and 2 is lost,
It is characterized in that:The obtaining value method of array resolution and Sidelobe Suppression level is in the step (2):Calculate analysis frequency range
Array resolution and Sidelobe Suppression level under interior all 1/3rd octave center frequencies, take wherein worst array and differentiate
Rate and Sidelobe Suppression level.
4. the microphone array optimum design method that a kind of consideration acoustic propagation correlation according to claim 1 is lost, its
It is characterised by:Constraints in the step (4) is:xi∈ Ω and | xi-xj|≥d;Wherein xi、xjIt is array element coordinate, M
It is array element number, i=1....M, j=1....M, d are minimum spacing limit value between unit, Ω is determined by array bore
Region.
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Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110298063A (en) * | 2019-05-10 | 2019-10-01 | 北方民族大学 | A kind of non-compact permeable boundary aerodynamic noise numerical integration calculation method |
CN110457796A (en) * | 2019-07-29 | 2019-11-15 | 南京大学 | The optimization method of random plane difference microphone array element position |
CN110851913A (en) * | 2019-10-10 | 2020-02-28 | 中国直升机设计研究所 | Helicopter aerodynamic noise determination method |
CN111475961A (en) * | 2020-04-21 | 2020-07-31 | 中国空气动力研究与发展中心低速空气动力研究所 | Adaptive array type optimization design method of microphone array |
CN112818472A (en) * | 2021-02-25 | 2021-05-18 | 西北工业大学 | Civil aircraft flight test subject arrangement and optimization method |
CN114325214A (en) * | 2021-11-18 | 2022-04-12 | 国网辽宁省电力有限公司电力科学研究院 | Electric power online monitoring method based on microphone array sound source positioning technology |
CN114564064A (en) * | 2022-03-07 | 2022-05-31 | 中车株洲电力机车研究所有限公司 | Maximum power point dynamic tracking method and system under photovoltaic array multi-peak value |
CN114584895A (en) * | 2022-05-07 | 2022-06-03 | 之江实验室 | Acoustic transceiving array arrangement method and device for beam forming |
CN115169250A (en) * | 2022-09-05 | 2022-10-11 | 湖北工业大学 | Microphone array optimization method and system based on improved mixed integer differential evolution |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103487135A (en) * | 2013-09-26 | 2014-01-01 | 中国航天空气动力技术研究院 | Microphone array optimum design method for closed wind channel aerodynamic noise measurement |
CN104729509A (en) * | 2015-03-24 | 2015-06-24 | 张韧 | Route planning method based on non-dominated sorting genetic algorithm II |
-
2017
- 2017-02-24 CN CN201710103386.2A patent/CN106709216B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103487135A (en) * | 2013-09-26 | 2014-01-01 | 中国航天空气动力技术研究院 | Microphone array optimum design method for closed wind channel aerodynamic noise measurement |
CN104729509A (en) * | 2015-03-24 | 2015-06-24 | 张韧 | Route planning method based on non-dominated sorting genetic algorithm II |
Non-Patent Citations (2)
Title |
---|
MIN JIANG 等: "Experimental and numerical investigation on sound generation from airfoil-flow interaction", 《APPLIED MATHEMATICS》 * |
周家检 等: "应用于运动声源的麦克风相阵列测量技术", 《2016年度全国气动声学学术会议》 * |
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